Jianyong Ao¹, Yanping Li¹, Shengqing Hu¹, Songyu Gao², Qi Yao^2,*

Energy Engineering, Vol.121, No.12, pp. 3825-3841, 2024, DOI:10.32604/ee.2024.055250 - 22 November 2024

Abstract Blades are essential components of wind turbines. Reducing their fatigue loads during operation helps to extend their lifespan, but it is difficult to quickly and accurately calculate the fatigue loads of blades. To solve this problem, this paper innovatively designs a data-driven blade load modeling method based on a deep learning framework through mechanism analysis, feature selection, and model construction. In the mechanism analysis part, the generation mechanism of blade loads and the load theoretical calculation method based on material damage theory are analyzed, and four measurable operating state parameters related to blade loads are… More >

Dattu Ghane^*, Vishnu Wakchaure

Energy Engineering, Vol.121, No.11, pp. 3199-3220, 2024, DOI:10.32604/ee.2024.050952 - 21 October 2024

Abstract Wind energy provides a sustainable solution to the ever-increasing demand for energy. Micro-wind turbines offer a promising solution for low-wind speed, decentralized power generation in urban and remote areas. Earlier researchers have explored the design, development, and performance analysis of a micro-wind turbine system tailored for small-scale renewable energy generation. Researchers have investigated various aspects such as aerodynamic considerations, structural integrity, efficiency optimization to ensure reliable and cost-effective operation, blade design, generator selection, and control strategies to enhance the overall performance of the system. The objective of this paper is to provide a comprehensive design… More >

Shuang Kang¹, Yinchao He^1,2, Wenwen Li^1,*, Sen Liu²

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 933-949, 2024, DOI:10.32604/cmc.2024.056614 - 15 October 2024

Abstract To address the issues of low accuracy and high false positive rate in traditional Otsu algorithm for defect detection on infrared images of wind turbine blades (WTB), this paper proposes a technique that combines morphological image enhancement with an improved Otsu algorithm. First, mathematical morphology’s differential multi-scale white and black top-hat operations are applied to enhance the image. The algorithm employs entropy as the objective function to guide the iteration process of image enhancement, selecting appropriate structural element scales to execute differential multi-scale white and black top-hat transformations, effectively enhancing the detail features of defect… More >

Husam AlShannaq, Aly Mousaad Aly^*

Structural Durability & Health Monitoring, Vol.18, No.6, pp. 707-737, 2024, DOI:10.32604/sdhm.2024.054731 - 20 September 2024

Abstract Wind turbines have emerged as a prominent renewable energy source globally. Efficient monitoring and detection methods are crucial to enhance their operational effectiveness, particularly in identifying fatigue-related issues. This review focuses on leveraging artificial neural networks (ANNs) for wind turbine monitoring and fatigue detection, aiming to provide a valuable reference for researchers in this domain and related areas. Employing various ANN techniques, including General Regression Neural Network (GRNN), Support Vector Machine (SVM), Cuckoo Search Neural Network (CSNN), Backpropagation Neural Network (BPNN), Particle Swarm Optimization Artificial Neural Network (PSO-ANN), Convolutional Neural Network (CNN), and nonlinear autoregressive… More >

Zhan Wang¹, Liang Li^2,*, Long Wang¹, Weidong Zhu³, Yinghui Li⁴, Echuan Yang⁵

Energy Engineering, Vol.121, No.10, pp. 2981-3000, 2024, DOI:10.32604/ee.2024.048161 - 11 September 2024

Abstract A dynamic pitch strategy is usually adopted to improve the aerodynamic performance of the blade of a wind turbine. The dynamic pitch motion will affect the linear vibration characteristics of the blade. However, these influences have not been studied in previous research. In this paper, the influences of the rigid pitch motion on the linear vibration characteristics of a wind turbine blade are studied. The blade is described as a rotating cantilever beam with an inherent coupled rigid-flexible vibration, where the rigid pitch motion introduces a parametrically excited vibration to the beam. Partial differential equations More > Graphic Abstract

Xin Guan^*, Yuqi Xie, Shuaijie Wang, Mingyang Li, Shiwei Wu

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.9, pp. 2045-2058, 2024, DOI:10.32604/fdmp.2024.049671 - 23 August 2024

Abstract The effects of the erosion present on the leading edge of a wind turbine airfoil (DU 96-W-180) on its aerodynamic performances have been investigated numerically in the framework of a SST k–ω turbulence model based on the Reynolds Averaged Navier-Stokes equations (RANS). The results indicate that when sand-induced holes and small pits are involved as leading edge wear features, they have a minimal influence on the lift and drag coefficients of the airfoil. However, if delamination occurs in the same airfoil region, it significantly impacts the lift and resistance characteristics of the airfoil. Specifically, as More >

Xin Guan^1,2,*, Mingyang Li¹, Shiwei Wu¹, Yuqi Xie¹, Yongpeng Sun¹

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.9, pp. 2029-2043, 2024, DOI:10.32604/fdmp.2024.049499 - 23 August 2024

Abstract The focus of this research was on the equivalent particle roughness height correction required to account for the presence of ice when determining the performances of wind turbines. In particular, two icing processes (frost ice and clear ice) were examined by combining the FENSAP-ICE and FLUENT analysis tools. The ice type on the blade surfaces was predicted by using a multi-time step method. Accordingly, the influence of variations in icing shape and ice surface roughness on the aerodynamic performance of blades during frost ice formation or clear ice formation was investigated. The results indicate that More >

Wanrun Li^1,2,3,*, Wenhai Zhao¹, Tongtong Wang¹, Yongfeng Du^1,2,3

Structural Durability & Health Monitoring, Vol.18, No.5, pp. 553-575, 2024, DOI:10.32604/sdhm.2024.050751 - 19 July 2024

Abstract The accumulation of defects on wind turbine blade surfaces can lead to irreversible damage, impacting the aerodynamic performance of the blades. To address the challenge of detecting and quantifying surface defects on wind turbine blades, a blade surface defect detection and quantification method based on an improved Deeplabv3+ deep learning model is proposed. Firstly, an improved method for wind turbine blade surface defect detection, utilizing Mobilenetv2 as the backbone feature extraction network, is proposed based on an original Deeplabv3+ deep learning model to address the issue of limited robustness. Secondly, through integrating the concept of… More > Graphic Abstract

Yuanjun Dai^1,4,*, Dong Wang¹, Xiongfei Liu², Weimin Wu³

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.7, pp. 1665-1665, 2024, DOI:10.32604/fdmp.2024.053146 - 23 July 2024

Abstract This article has no abstract. More >

Yuanjun Dai^1,2,3,*, Zetao Deng¹, Baohua Li², Lei Zhong¹, Jianping Wang¹

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.7, pp. 1517-1537, 2024, DOI:10.32604/fdmp.2024.046828 - 23 July 2024

Abstract A combined experimental and numerical research study is conducted to investigate the complex relationship between the structure and the aerodynamic performances of an Archimedes spiral wind turbine (ASWT). Two ASWTs are considered, a prototypical version and an improved version. It is shown that the latter achieves the best aerodynamic performance when the spread angles at the three sets of blades are α = 30°, α = 55°, α = 60°, respectively and the blade thickness is 4 mm. For a velocity V = 10 m/s, a tip speed ratio (TSR) = 1.58 and 2, the maximum C values More > Graphic Abstract